{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2021-11-26T17:43:08.042Z"
    }
   },
   "outputs": [],
   "source": [
    "import fdtd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy as sp\n",
    "import re\n",
    "import os\n",
    "import sys\n",
    "import scipy.constants as sc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis and Process the properties of materials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-26T15:57:56.656697Z",
     "start_time": "2021-11-26T15:57:56.653011Z"
    }
   },
   "outputs": [],
   "source": [
    "fdtd.set_backend(\"numpy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-26T15:57:58.488345Z",
     "start_time": "2021-11-26T15:57:57.883113Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.381387909418767\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(1, 10, '$\\\\mu = $4.381387909418767$\\\\lambda$ + -0.14791991846258007')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get Refractive Data of W:\n",
    "from scipy.optimize import leastsq\n",
    "df = pd.read_csv(\"./indexdata/W-Refractive Index.csv\")\n",
    "lambdas = df.iloc[:, 0]\n",
    "permittivities = df.iloc[:, 1]\n",
    "permeabilities = df.iloc[:, 2]\n",
    "plt.title(r\"$\\epsilon_r$ and $\\mu$ to $lambda$ in material W\")\n",
    "plt.plot(lambdas, permittivities, label=r\"$relative\\ \\epsilon-\\lambda$\")\n",
    "plt.plot(lambdas, permeabilities, label=r\"$\\mu-\\lambda$\")\n",
    "plt.xlabel(r\"$\\lambda :(\\mu m)$\")\n",
    "plt.xlim(0.3, 5)\n",
    "\n",
    "#plt.annotate(r\"$\\mu\\ Starts\\ fitting\\ in\\ a\\ line$\", xy=(1.3, 5), xytext=(0.7, 1), arrowprops=dict(arrowstyle=\"->\"))\n",
    "\n",
    "X = lambdas\n",
    "Y = permeabilities\n",
    "def delta(initxy):#p is an arg list\n",
    "    k, b = initxy\n",
    "    return (Y - X*k - b)\n",
    "\n",
    "r = leastsq(delta, [0, 0])\n",
    "k, b = r[0]\n",
    "\n",
    "\n",
    "pred_Y = lambdas * k - b\n",
    "plt.ylim(0, 22)\n",
    "plt.plot(lambdas, pred_Y, linestyle=\":\", markersize=0.1, label=r\"fitting $\\mu - \\lambda$\")\n",
    "plt.legend()\n",
    "formula_expr = r\"$\\mu = $\" + str(k) + \"$\\lambda$ + \" + str(b)\n",
    "print(k)\n",
    "plt.annotate(formula_expr, \n",
    "             xy=(1, 20), xytext= (1, 10),\n",
    "            rotation=np.arctan(k))\n",
    "\n",
    "# TODO!!!:  拟合磁导率，\n",
    "# TODO!!!:  拟合介电常数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x291abf99bb0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits import mplot3d\n",
    "lambdas = df.iloc[:, 0]\n",
    "ns = df.iloc[:, 1]\n",
    "ks = df.iloc[:, 2]\n",
    "fig = plt.figure()\n",
    "ax = plt.axes(projection='3d')\n",
    "ax.plot3D(ns, ks, lambdas, label=['n', 'k', 'l'])\n",
    "ax.set_title(r'$n$, $k$, $\\lambda$')\n",
    "ax.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-4c36117cd796>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mepsilon0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mepsilon_0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mwavelengths\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;31m# Fitting n(lambda) , lambdaa in 0.3 -- 5 um\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mn_1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "epsilon0 = sc.epsilon_0\n",
    "c = sc.c \n",
    "wavelengths = df.iloc[:,0]\n",
    "# Fitting n(lambda) , lambdaa in 0.3 -- 5 um\n",
    "n_1 = df.iloc[:,1]\n",
    "k_given = df['k']\n",
    "ks = 2*np.pi * n_1/ (wavelengths)\n",
    "#Fitting N-Lambda\n",
    "plt.xlim(0.28, 52)\n",
    "plt.ylim(2.5, 200)\n",
    "plt.title(\"W's N - wavelength\")\n",
    "#plt.plot(wavelengths, n_1)\n",
    "def IRsquare(n1, n2):\n",
    "\treturn ((n1 - n2) / (n1 + n2))**2\n",
    "\n",
    "def ITsquare(n1, n2):\n",
    "\treturn ((4*n2) / (n1 + n2))**2\n",
    "\n",
    "\n",
    "plt.plot(wavelengths, k_given, label=\"k_data_given\")\n",
    "#plt.plot(wavelengths, ks, label=r\"k=2\\pi n/\\lambda\")\n",
    "plt.legend()\n",
    "plt.xlabel(r\"\\lambda (\\mu m)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def \n",
    "def permittivity(l):\n",
    "        \n",
    "def Wlambda2coff(wavelength):\n",
    "    return permittivity(wavelength), permeability(wavelength)\n",
    "thickness = 50e-9\n",
    "width = 1.0\n",
    "length = 1.0\n",
    "unitwidth = 10e-9\n",
    "permittivity = # Electronic constant\n",
    "permeability = # Magnetic conduct coff\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-26T06:12:02.273613Z",
     "start_time": "2021-11-26T06:12:02.064382Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-26T15:35:08.248429Z",
     "start_time": "2021-11-26T15:35:08.230653Z"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'dirs' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m-------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-63-c10b02fd98a9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mdir\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdirs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'dirs' is not defined"
     ]
    }
   ],
   "source": [
    "for dir in dirs:\n",
    "    print(dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-26T16:00:57.199138Z",
     "start_time": "2021-11-26T16:00:57.195751Z"
    }
   },
   "outputs": [],
   "source": [
    "# Assemble all refractive indexes\n",
    "import sys, os\n",
    "import os.path as op\n",
    "import re "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2021-11-26T17:42:51.591Z"
    }
   },
   "outputs": [],
   "source": [
    "dirs = [op.join('indexdata', p) for p in os.listdir('./indexdata')]\n",
    "pattern = r\"/[\\w\\d]+[^-]\"\n",
    "regex = re.compile(pattern)\n",
    "c = 2.998e8\n",
    "mus = []\n",
    "\n",
    "for cur_dir in dirs[: 20]:\n",
    "    table = pd.read_csv(cur_dir)\n",
    "    print(table)\n",
    "    result = regex.search(cur_dir)\n",
    "    mu = 1/ (c**2 * table.iloc[:, 1])\n",
    "    mus.append(mu)\n",
    "    plt.plot(\n",
    "            1e3 *np.array(table.iloc[:, 0].values), \n",
    "            table.iloc[:, 1], \n",
    "            label=result.group(0))\n",
    "\n",
    "#plt.yticks([])\n",
    "plt.ylim([0, 10])\n",
    "plt.xlim([0.2e3, 0.6e3])\n",
    "plt.ylabel(r\"Refractive Index $n$\")\n",
    "plt.xlabel(r\"$(\\lambda)$:nm $(0.3\\mu m\\rightarrow 0.5\\mu m)$\")\n",
    "plt.title(\"Refractive Index - Wavelength\")\n",
    "#plt.legend(bbox_to_anchor=(1.02, 1))\n",
    "plt.legend(bbox_to_anchor=(1.0,-0.2), ncol=int(len(dirs)/4)-2)\n",
    "plt.figure(figsize=(300, 500))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2021-11-26T16:24:13.392Z"
    }
   },
   "outputs": [],
   "source": [
    "dirs = [op.join('./indexdata', p) for p in os.listdir('./indexdata')]\n",
    "# 等下把read_csv 全部提前导出，减少重复读取的耗时\n",
    "pattern = r\"/[\\w\\d]+[^-R]\"\n",
    "regex = re.compile(pattern)\n",
    "for cur_dir in dirs[:20]:\n",
    "\ttable = pd.read_csv(cur_dir)\n",
    "\tresult = regex.search(cur_dir)\n",
    "\tplt.plot(\n",
    "\t\t\t1e3 *np.array(table.iloc[:, 0].values), \n",
    "\t\t\ttable.iloc[:, 2], \n",
    "\t\t\tlabel=result.group(0))\n",
    "\n",
    "#plt.yticks([])\n",
    "plt.ylim([0, 8])\n",
    "plt.xlim([0.2e3, 0.8e3])\n",
    "plt.ylabel(r\"permittivity  $epsilon$ in materials\")\n",
    "plt.xlabel(r\"$(\\lambda)$:nm $(0.3\\mu m\\rightarrow 0.5\\mu m)$\")\n",
    "plt.title(r\"$\\epsilon$ - $\\lambda$\")\n",
    "#plt.legend(bbox_to_anchor=(1.02, 1))\n",
    "plt.legend(bbox_to_anchor=(1.1,-0.2), ncol=int(len(dirs)/4)-1)\n",
    "plt.figure(figsize=(800, 800))\n",
    "arrow = plt.arrow(200, 4, 100, 1, width=0.1)\n",
    "#plt.annotate(\"A Peak\", xy = (200, 2), \n",
    "#\t\txytext = (300, 3), \n",
    "#\t\tarrowprops = dict(arrowstyle=\"->\"))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2021-11-26T16:24:39.590Z"
    }
   },
   "outputs": [
    {
     "ename": "Error",
     "evalue": "Session cannot generate requests",
     "output_type": "error",
     "traceback": [
      "Error: Session cannot generate requests",
      "at S.executeCodeCell (c:\\Users\\Shaw\\.vscode\\extensions\\ms-toolsai.jupyter-2021.10.1101450599\\out\\client\\extension.js:66:301742)",
      "at S.execute (c:\\Users\\Shaw\\.vscode\\extensions\\ms-toolsai.jupyter-2021.10.1101450599\\out\\client\\extension.js:66:300732)",
      "at S.start (c:\\Users\\Shaw\\.vscode\\extensions\\ms-toolsai.jupyter-2021.10.1101450599\\out\\client\\extension.js:66:296408)",
      "at processTicksAndRejections (internal/process/task_queues.js:93:5)",
      "at t.CellExecutionQueue.executeQueuedCells (c:\\Users\\Shaw\\.vscode\\extensions\\ms-toolsai.jupyter-2021.10.1101450599\\out\\client\\extension.js:66:312326)",
      "at t.CellExecutionQueue.start (c:\\Users\\Shaw\\.vscode\\extensions\\ms-toolsai.jupyter-2021.10.1101450599\\out\\client\\extension.js:66:311862)"
     ]
    }
   ],
   "source": [
    "print(dirs)\n",
    "\n",
    "for dir in dirs:\n",
    "    df = pd.read_csv(dir)\n",
    "    filename = regex.search(dir)\n",
    "    if np.isnan(df['k'][4]):\n",
    "        continue\n",
    "    else:\n",
    "        \n",
    "        mu_r = df['k']\n",
    "        wavelength = df.iloc[0] * 1e-6\n",
    "        n = df.iloc[1] # refractive index\n",
    "        k0 = 2 * np.pi / wavelength# k in air(vaccum)\n",
    "        kz = wavelength / n\n",
    "        epsilon = kz * sc.epsilon_0\n",
    "        mu = mu_r * sc.mu_0\n",
    "        print(f\"====={filename}=====\\n{n.values=} {mu_r.values=} {kz.values=} {epsilon.values=} {mu=}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-26T05:50:39.646454Z",
     "start_time": "2021-11-26T05:50:38.015Z"
    }
   },
   "outputs": [],
   "source": [
    "dirs = [op.join('./indexdata', p) for p in os.listdir('./indexdata')]\n",
    "pattern = r\"\\\\[\\w\\d]+[^-R]\"\n",
    "regex = re.compile(pattern)\n",
    "c = 2.998e8\n",
    "mus = []\n",
    "\n",
    "cur_dir = dirs[19]\n",
    "table = pd.read_csv(cur_dir)\n",
    "result = regex.search(cur_dir)\n",
    "mus = 1 / np.array([c * epsilon for epsilon in table.iloc[:, 2]])\n",
    "\n",
    "plt.plot(\n",
    "\t\t1e3 *np.array(table.iloc[:, 0].values), \n",
    "\t\tmus, \n",
    "\t\tlabel=result.group(0)\n",
    ")\n",
    "\n",
    "#plt.yticks([])\n",
    "#plt.ylim([0, 1e-8])\n",
    "#plt.xlim([0.2e3, 0.6e3])\n",
    "plt.ylabel(r\"Refractive Index $n$\")\n",
    "plt.xlabel(r\"$(\\lambda)$:nm $(0.3\\mu m\\rightarrow 0.5\\mu m)$\")\n",
    "plt.title(r\"$\\mu$ - Wavelength in \" + result.group(0))\n",
    "#plt.legend(bbox_to_anchor=(1.02, 1))\n",
    "plt.legend(bbox_to_anchor=(1.1,1.2), ncol=int(len(dirs)/4)-1)\n",
    "plt.figure(figsize=(300, 500))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-11-26T05:50:39.663526Z",
     "start_time": "2021-11-26T05:50:38.046Z"
    }
   },
   "outputs": [],
   "source": [
    "fdtd.set_backend(\"numpy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting and Optimization\n",
    "We have to fit the lambda - n, lambda - mu, lambda - k\n",
    "and optimizate the constraints!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now We Start fitting n - $\\lambda$**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.36046802e-09 -7.79511749e-08  1.43119036e-06  4.04347666e-06\n",
      " -6.05261290e-04  1.15962628e-02 -1.18792604e-01  7.46348225e-01\n",
      " -2.91371853e+00  6.64133352e+00 -7.04686484e+00 -2.54507100e-01\n",
      "  4.43769389e+00  1.81284896e+00]\n",
      "          13             12             11             10             9\n",
      "1.36e-09 x  - 7.795e-08 x  + 1.431e-06 x  + 4.043e-06 x  - 0.0006053 x\n",
      "           8          7          6         5         4         3\n",
      " + 0.0116 x - 0.1188 x + 0.7463 x - 2.914 x + 6.641 x - 7.047 x\n",
      "           2\n",
      " - 0.2545 x + 4.438 x + 1.813\n"
     ]
    },
    {
     "data": {
      "image/png": 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NpH06ErDFQQR/AuZ2xo9+7OdzlLvfaGHyyUmE2pofFtlURER6AAVsIiIiIlVO03qIiIiIVDkFbCI9gGXk3MxbJyvPZlbOyX3ipJDz4989E/uqM7NrLOTYe8HMcrPY725ms82s2cyOTKw/0kI+xectTOR6dOK5PeM2C8xsWhzWn8vjd0Us7zwzG13mOSm4vaXkvyxxv6mvMWW9rLycE8zs7cT5Pinx3EXxPCzIO0dmKTkNzeyY+Nrmm9lTZjYisU1qTkLLyKtqZv3i+Z8f128zNUGJ52a1/IhmtqmF3J+59+fp7dhvap7OlPV+b2bvWV7OSguZRWbH8/2Ehbn8iuWXzLoWN8XXt8DChMT9Es+l5pe0duTRLfG8bGHp+VZLeo8W2O8fE+dlmYWpLrLWTcsjm/X9cHJ8f+Wuw/Z5+2qTMzQuOzOezwVmNt1C947M61DkM7EkcfzGdpyXrO/Qo2IZV1mRXNBdqtQRILrppltlbmTk3ExZLyvP5mTSc06OAgbH+zsCryeeOw/4cby/BnEaA8KoyeGEKS2OTKy/NXEqEGAwYZTrenHb12id8uF8Wke97h/LabHcM8s8LwW3Jy//ZRn7TX2NKeul5qwkYzoNwrxvDxKmChgAPEvraMKsnIZfTlz3/ZKvkYychGTkVSWk27k53l+bMJJ0aBnnJTU/YjwHo+M6AwkjEbcvdb+J9+JQMqbMSKy3F2Fy4bvzlr8IbBfvf5sw8W7+ti35JYtci/3je8oII2FzeU3Xo5Py6JZxXrLyrZb0Hi3xGLcDxxd4frXPEdnfD8kcxAcDv8/bV37O0E0IaanWSrzeCUWuQ6HPRMH3TwnnIus7dDvCJMWPUiQXdFfeVMMmUv2ycm62sAJ5NrN4yI2Yy9P4PLCWteY8/Vfgf+N6q9x9eby/xMOosVV5+3rR3V+K95cRJgLdkDB9QZO3Zn14kJhzkpBv7wYPniFMpLpxfD1nWWv+z9XS1pSwfVr+y5JkvcaU9UrKWZmwPWEet2YPE3LOI04QS0ZOQ3d/KnfdaZsjdV0ychJ6Rl5VwpyBAyzUcK4FNBGmouhQfkR3f8PdZ8djf0iYAiUtZVgmz8jTmbLew8CHaU9RPP9oMkdq5rVw93vje8oJP+C589dpeXStg/lWS32PFmNhtPOehOkl0p7P+hxlfT9k5SDGUnKGRn0J3z25Oc+WxX2lXoesz0SR1/lPsXZ2Vqxd3DZlnUK5ihe5+5+LHaerKWATqX6pOTfzFMuzWSzn5BGEmfg/s9iEBvwoNrv8xsw2StkmlZmNJdQ0vAwsB/ommhGOpHUG8NR8e2b2NUKt4ljCnEVjzKxNaq8i22flv+xOR8TzfZuFWech1Lzsa2ZrW2j224PWc1FKPscTac2RWmpOwmRe1dsIqXDeIEzs/BN3/7t1PD9iCwsjp0cR0oB1p5OAey3kHz2OMCFqslyb0za/ZKFrkdumX9zX7+Oizsyj29F8q53lUMJkxB/kP5H1OSr2/WBmp5rZy4T58HJN+6k5Q939deAnhPfjG4S8v22Cw5TrkJT8TEAIEB+I12dSYvk1hIwnY+Lr+XnKvkrNVVwxCthEql9qzs0yti+Yc9LMdiA0nX0rcbwhwFPuPpowoWRJc7rF/1JvJMzqvyr+pzoeuMzM/kSoHSmWi/Nr8fYcIRPEtpSX/zM1/2U3uovQ1DicUKM4DULNFyHF11OEmp6naT0XBfM5mtkehB+n78ZFRXMS2up5VcfG4w0mBC//YWZb0vH8iLnjrUNoXjsjLQDoYmcSsl4MIcxkf2ne823ySxa5Fjk/J9TC/TE+7sw8uh3Nt9pZkrWO+bI+RwW/H9z9Knf/J8J79Qdx8WRScobGfx4PIbwfBxNqgPPTWuVfh9y2+Z8JCBMNjyY0lZ5qoa/fOoRm1N/E8/1LQjN+j6NcoiJVxlbPc5mVc/PZxGavk5Fn0wvknDSzIYQcese7+8tx8TvAx7Tm2PsN4YuxWLk/R5g1/PuxiZJ4/KcJyZeJtWe5H7msfHsG/K+7/zJv/6cSmoch9G/J2j4z/2Wx19AZ3P2dxMMphJqG3HMXABcAWBg8kmsqzsznaGbD4372S+y7YE5CS8+r+i+EPkX/AN4ysycJ/as6mh8xVwtyO3CTu99BEZbIK+ruJxVbv8i+NiTMUJ87F7ewem1Mfn7JQtcCMzuX0KT/rcQmnZlHt6P5Vjss1iyOBQ7LWCX1cwR8j9K+H24m/LMIGTlDgTeBv7j727FMdxCCq1/Hx2nXIeszkauxw93fMrPfxtc3h1BTOTJvH30ImQogBM2/oECu4qrgFeo8p5tuupV2IyPnZsp6WXk2U3NOEr445xJm187f183AnvH+BOA3ec9Ppe2ggzpCqp0zUvaV65y9Zlwnt98DaDto4E9x+dcITWrrxMebkDLQImv7vHUm0LazdDn5P9u8xgLr5Q86SJ7vwwiJ7CF0Tt8g3h9OSHmTGxyQldNwM0JfsS+nHDcrJ2FWXtXvAtfH+wPiOsPpYH7EeP5vAC5P2aZofsS89ZdQpNM4q6dA6ktoes8NbDkRuD3xfFZ+yaxrcRKh5m2tvON2Zh7dDuVbLfQeBV4o8VyfTPxeKWHdCbT9HKV+P9A2B/FBhIA8f1+TaR108M+EPm1rx/fRNELTZaHrkPqZiO/pgYn7TwH7xsdPESYWJx5nRMbrTP0OTTz/KG1TC5aV37ajt245iG666db+Gxk5N+NzcxL3s/JspuacJDRXfERr/s05tP5ob07ITzgv/kjlRsbtRKhR+IhQE/d8XH4sIfdpcl8j43OXEDqi/5m2OfkMuCqWd37eF+HpcVkux98/ZZyX1O0T60yg7Q/NHIrk/8x6jSnnOytn5f8SfoTmEnKXbhuX96c19+YzJH6kyc7nOAV4N3FOGxPbjCQ9J2FWXtV1CAHA87EMyYCh3fkRgd0IfYfmJY6Z+2ehaH7EuF5Wns763P34+I+EvnufxPW/HpcfFs/dXMKP6paJbSaTl1+yyLVojq8x91qS+UQ7K49uR/OtZn0OBwF/LvF75VFiQJP3HTIlZd0JtP0cZX0//JTWXKiPkB64TiYxap3Qr+2FeE5vTLzG1OtAxmcinqu5tOZUTeZL3YJQ6zo3nu+s3KRZ36GHxfP9GaFW8P7E+kXz23bWTZkORKQmmPJ/djsrIT+idB4zO5AQrF5R6bLUAuvm/LYK2ERERESqnEaJioiIiFQ5BWwiVc7M/t1C2p95ZvZwnFMqbb2j4zrPm9lFieVZ6aQ2t9Z0Ps+b2clx+UBrTVszx8yWm9nlhfYVn7s47meRtU3r8qiFFDO5/X0hLi+Yxqad52oLS0/ls2Z8vDg+P7TM/aam4klZb7i1puiabzHNTuL5GWa2IPE4NeWNmY1NnK+5ccQmZtbfwgS3c+N25yW2uS4uz83/tk5cPsFSUmVZgXRiHWEp6avi8oLpm4rsM/M9Werx43N9LMxblxwpbZaeFmxdM7srca4nJrbZzMweiOsvzH8/WV4qqrj+I/HY8yymh8t7vk3KpvYq9F4vdG5K2G9J75es91vi+bRUV1nfEamvxUJ6quT7YZWZjYzPZaWM65LzUmh7C/NevmWJz3yHdFdnOd100619N8KknmvH+6cAt6SsswFh8skN4+NphCkdIDudVB2tHXzXIYykG5yy71nA7kX29WXgScLouz6EjvPj4nOPkj4goGAamyLnZAKxk3/e8qxUPt+mtfP9+LRzWOR4qal48tbpS+iEPSJxTfoknj+cMDpwQWJZasobwqi53KjFjQmZI/oSBlrkRs/2I4ym3TnlfF4KnJM4V2mpslLTiZVxTpakLMtKX1UwfVM7PhMt78lSjp94/t/jNUiOMs1KC/ZftKb12hD4O1CXeE/vk/jsrJ3Y32qpqAgTt56SKOOSvHK3SdlU4jkYCjyasjz1vV7s3JRwvJLeL1nvt8Tzaamu2rz/i72WvHWGAS8nHmeljOuq85K5PSEbyWgSn/mO3FTDJlLl3P0Rd/84PsxKxbIl8JLH+YyAh4gpoDw7nVSTu38WH65JSo27hYlBv0AYnZe5L8Iowf7EIJAQTLxJAZ6RxibWglxirampvpW+h9XKmpnKhzA557R4/zZgr7h+qVJT8eT5GjDP3efG9d7xOFFrrO36d+DHyQ08I+WNu3/srTPc9yeeGw9yNQf94i33XC7NlBFSTxXsoOzZ6cSwkAT7MQszxt9vMeVXCQ4hJX0VBdI3lSv/PVni8XNzDh7A6lkFUtOCEc7fwHg+1yEEbM0WaoL7uvuDcf0Vuc+nZaSiokDqLMtI2WSlpQtLk/Vezzw3pSj0fimVlZ8yrpTPbQPh/ZQrZ1bKuLLPS4nXIHN7d3+c8L7pFArYRHqW/FQsOYuBbcxsqIWcfIeSl2onjZltambzCNNAXOStuUVzcv+JFvvxf5owjP+NeLvf3RclVrk+fun9d/IL11LS2MTX+L6HtD07Ad80sy2KvRYKp/JpSR8Un38/rl+UlZ6qa2vAY4Az28ySP9o/ImSY+Dhlu6zj/rOZPU+YruLk3OuKAe0cwg/mg946YSxmdj3wN8LcY8kZ9tNSZSWP1ZJOzMIkuD8j1KCOIWRduKDEYmelaSqa1qoMhd6ThY5zOSGQyv9nIyst2JWEGtBlhGtwegzqtgbeM7M7LDRxXpL4Ic9KRTUZONZC6qx7CYnoc4H8aimbrPR0YWmy3uuddg2sbfq5NKu936x4yri074hSPrdHk52tIams81LGNejM93ZBCthEeggLKVvqCf/Bt+EhGfIphLmd/kho3iyWAgp3f81DCqWtgBNSApHxlPBlaGZbEX7chhC+rPY0s6/Ep49x92GEbAdfIeQFzB0/LY3N14DjY1Ayk/Cl+iUz2yDXZwU4Hzg50YdlWLEydkCpqbr6EuYkOyb+PczM9op9a/7J3X9bzkHdfaa770AIWr9nsT+cu6+MPyBDgLFmtmNim4mE5qpFhB8byEiVlWN56cQITbQ7Ag/Gc/0DWpPOfz9xDQYnzv9V5by2DirpPZlkYbqLt9x9VsrTWWnBvk6Y52swYc67Ky1MDdOX8D7+T8K12RKYYIVTUTUAUz2kztofuDEGMJNJSdlEgXRhZvbbuOxeoD5xDSbSDVLeL/my3m+FUsZlfkcUKcs/Ax+7e+f0EWurU1K2dSalphKpMrZ6airMbG/CpKVfTTRjtuHudxG+LLGQ+LhowJbYdpmFjrFfITYpmtkIQtNP2o9cvtyM/ivitvcRUtv80VvTxXxoIQXQWEKfoaRkGhsjzHZ+f8pxRsb9TyD8KEzOPRH/K1/P0lP55NIqLY01kOsSJhwtRampupYSch4uj+W5l9B/ZQXhh3UJ4Tv3C2b2qLuPK+Xg7r7IQgfqHQkT5eaWv2dmjxAyGyxILF9pZjcTapOu9wKpsiw9nZgRJmLdJaUsyXROSzwv3Q8F0lcVWL4ay0sb5O4/jMuLvSezjn8wcLCFzv79Cbk7f+3ux5KdFmwiYcJdJ6SX+guh5nIpYQLlV2KZ7iT0m/ob2amoTiRcJ9z96Rh8DyI7ZVNqurC4fW4AylBCEDgu4xzkv9cLXZvVxGAolx7uh+4+I+P9kl++rPdbZsq4At8RxT635QTv5Z6XjelgyrZO553QEU433XTruhshJ+HLJNK+ZKyX6yy9PqFmYOu856fSdqDAEGLal7jNi8CwxPMXAudlHCt/X0cT+s31JfSrepiQmqYvsYN+XH4boXkPMtLYAJMIM/f3i4+3BgbkHX8C6YMOUlP5EPJIJjsc3xrvl5RahiKpuhLncDZxwEA8HwfkrTOUlA7IrD7oYAtaBx1sTmiWG0ToM7ReXL4WoTb1QMIP/FZxuRFqAH8SH2elykpNJxaXLwZ2SVy3tBnrl6Qsy0pflZm+idA38LASPwuZ78lCx89bZxxtBx1kpQX7Ba3ZKzYi/AgPiq9nLq0DfK4HTk0pS3LQwX3AhHg/18xqeetPpjVlU9F0YWQPOsh6r2eem/g+2KTIuc9MP5e3Xur7LeXze2W8X+g7IvW1xMdrxGuyZUY58gcdlHVeSrkGpbznSPnMU2bKtpbtyt1AN910694b4Yf/TVpTscxIPDcncX86rel2kumrstLY7EMY1Tg3/p2Ud9xXiGmVSthXH8J/44vi8S+NywcQakrmETpV/zTxI5GaxiZ+Ef8Pod/QgvjcunnlmEB6wJaVyqd/fLw4Pr9lXF5SahmyU/EcTOiwnlvv2PiaFgAXp+ynzZc32Slvjkucm9nAoXH5cOC5WI4FtKbrWYMwSjd3zm4ijholO1VWoXRiI+PrzaX5+WbKa1mSca5WS18Vl6embwLuJgaHJVyHtPdk/jVIPX7i+XG0DdjWIz0t2GBC5/jcOT02sU3uszOf8M9LXcpxkgHb9vH6zI3n+Wsp60+mbcqmgunCyA7YUt/rWecmvnf+Sl7OzpT9Fnq/nA8cXOj9lvL5zQVshb4jCr2WcaQHg1kp48o6L6VcgxK2n07o0/uPWJYT4/KSUrbl35TpQERqlnVzahlZnZnd7+5lz30lnSP2gfxXd//3SpelVlg7U7YpYBMRERGpcholKiIiIlLlFLCJiIiIVDkFbCIiIiJVTgGbSA9gKUmrM9Y7wszcYiJxy04ivo21TZ78gZmdkdjPv1lIcv58nCMKCwnQr7eQ1HyumY1LrP97a02SfXWcRyuXMPqZeIxGCzOkY2brxwlA51lI/dIy+WsHzlFXJb1OTdCest6Zcb0FZjY9zrWFmZ0Wj+1mNiix/jHx9c83s6fiHGMF95V4vqTk4kWuWUkJ7cs8V6kJ3s1si3hNFsdrVJd4na9aTARuZj8xsz07Wg6RXqncYaW66aZb999ISVqdss5AwlQMzxDn9CIjiXjedn0Ik35uHh/vQZhKJDclRm4eolMJE7FCyOU4i9aE2bkpJAy4nda50B6gdfqA/YnTEBCyNZwb729LCXOhJco7lO5Nep2aoD1vnU0I+SBz89rdSuu8W6NimZeQSBoPfBlYP97fD5hZbF/xccnJxYtcs6IJ7Quck6nAuJT3UWqC9/gakvPjnZLYbgKtUzxsDjxQ6c+bbrpV4001bCJVzrKTVuf7EXAR8GlugWckEc+zF/Cyu/81Pj6FMMP7Z3EfuWTY2wN/SCx7jxA84K2J3PsSfqxzx3HSk14n9/UCMNRiWiyrvqTXqQnaU/QF1oqzqK9NfK3u/py7L0nZ71MeUopBCLKHFNuXlZ9cPPOakZHQ3sw2NLPbzezZeNu1hNcOGQne4zXYk5hBg3CNDk3bQXwPbmBmXyzxmCI1QwGbSPW7nPSk1S3MbDSwqbvfk/JcahLxhPz0LlsTUsjMNLPHzGynuHwuIb1PXwvJ2MeQSMliZvcTavA+pPXH+QzgEjN7jTD7/vcS+zo8bjeWULMyxKo86XUWD6l1fgK8Spgo8313f6CMXZxImA2/2L7KSi5OxjWzwgntf0rIb7kTcATF/1HIyTrPGwDvJd53xc7/bKDUIFGkZihgE6liVjhpdW6dNYBLgf9Ie94zkojHbesIM8X/JrFJX0Ialp2Bs4BbYy3Jrwg/to2EIPIpEvlKPUx+ujGh6THXD+kU4Ex33xQ4E7guLr+QkPdzDiG4eC7uq2qTXhdiZusTavK2IMyQP8DMji1x2z0IAdt3C+3L2pdcPOuaFUpovzch0fkcYAYh7+Y6ZvZ1a038fjAwJT6eWdpZKtlb8XWLSIKSv4tUt13JTlqdM5CQGPzREFfxRWCGmR3s7slk4WlJxPcDZrv7m4n9LQXucHcH/mRmqwj9m94mBF0AmNlThFRDLdz9UzP7HSHgeBA4ATg9Pv0bYm1NbEKdGPdjhD5brxCSz1c06XU77Q38JZ4jzOwOQh+1XxfayMyGE87Jft6aNDtrX+9SZnLx2Ayads0KJbRfg5CCp6VpPbo/3jCzqYRr8Gji+azz/A4hOO8ba9mKnf/+wCcFnhepSaphE6li7v49dx/i7kMJTZd/yAvWcPf33X2Quw+N6z1DyOvXGEfn9QUws80JHfyXJDZvoG1zKITE63vEbbYm9ElbbmZrm9mAuHwfoNndF8bal43j8r6E/nYvxH0tA74a7+9JSKaMma2XGykInAQ8HoO4h4EjzewLcb3Px3KXYgYhQAQ4Mp4rj8vHWxhFugXwJUIuQczsYTPrjObRV4Gd4zkyQs3gokIbmNlmhIDpOHdPBr6p+3L3e9z9i4nr/HEM1nLb7BX3ux0h6Hk765rF83IXIR8jcduF8f4DtDapYmYjSzwHzwJfiu+5OsL7dUY81iOEawLhGv2uwH62JuTuFJGkSo960E033Uq7sXrS6paEy3nrPUrrKNHUJOLxuQGE2o9187avI9QMLYjb7BmXDyUkN15EGEW6eVy+EeHHOpeQ/Ge0jkzdjTAycS4wExgTl+9CqOn5MyFoWT9x/GpLep2VoH0wcG9ivfMIgeoCwijO3Cjb78TtmwkB7JS4fAqh1mxOvDUW21deuYomF8+6ZvG5zUlPaD8IuCUuX0gceZt37KnkjRKNy7MSvG8Zr8nieI3WTDw3gdZRov1iWfvm71s33Wr9plyiIlKTTEmvq4KZTSD8g3GahXkCR7v7f1e4WCJVR02iIlKT3H2BgrXKMrMzCSOHk9PC/L/KlUikeqmGTURERKTKqYZNREREpMopYBMRERGpcgrYRERERKqcAjYRERGRKqeATURERKTKKWATERERqXIK2ERERESqnAI2ERERkSqngE1ERESkyilgExEREalyCthEREREqpwCNhEREZEqp4BNREREpMopYBMRERGpcgrYRERERKqcAjYRERGRKqeATURERKTKKWATERERqXIK2ERERESqnAI2ERERkSqngE1ERESkyilgExEREalyCthEREREqpwCNhEREZEqp4BNREREpMopYBMRERGpcgrYRERERKqcAjYRERGRKqeATURERKTKKWATERERqXIK2ERERESqnAI2ERERkSqngE1ERESkyilgExEREalyCthEREREqpwCNhEREZEqp4BNREREpMopYBMRERGpcgrYRERERKqcAjYRERGRKqeATURERKTKKWATERERqXIK2ERERESqnAI2ERERkSqngE1ERESkyilgExEREalyCthEREREqpwCNhEREZEqp4BNREREpMopYBMRERGpcgrYRERERKqcAjYRERGRKqeATSrKzP7XzM4ocd0/mdkOXVyk3LGmmtmPCzy/jZnNMbMPzew7Zva8mY3rxONX5XkREZHKUMAmFWNmGwLHA7/MW77IzJamBCE/Ac7vrvIVcTbwiLsPdPcr3H0Hd38UwMyWmNne7d1xDz8vIiLSBRSwSSVNAO5190/ylu8IvAgcmbd8BrCHmX2xG8pWzObA81207wn03PMiIiJdQAGbdCkz+5aZ3WtmV5nZcjNbZmb7xKf3Ax7L38bdVwJPAMPzln8KzAK+nnGsc8zs5dhMudDMDst7fomZ/aeZzTOz983sFjPrH58bZWaz47a3AP0LvKY/AHsAV5rZCjPbOlerZmY3ApsBd8Xnzq70eRERkZ5PAZt0tRHAzoRaoC8Qmvm+G58bBvw5fwMzWwtoiNvmW5SxHOBl4CvAusB5wK/NbOO8db4B7AtsQQh8JphZHXAncCPweeA3wBFZL8jd9wT+CJzm7uu4+4uJ544DXgUOis9dnLGb7jwvIiLSwylgk642HLjQ3e9391XAwsRz6wEfpmxzAbAU2NLM1sl77sO43Wrc/TfuvszdV7n7LcBLwNi81a6I6/wduAsYSQic+gGXu/s/3P024NkyXmN7dNt5ERGRnk8Bm3QZMzNCbdFdicU70hqcvAsMzNtmF+AoQg3X+3H7pIHAexnHOz6O3HzPzN6LxxqUt9rfEvc/BtYBBgOvu7snnvtrodfWEd19XkREpOdTwCZdaSjQl7bNe6OAOfH+PGDr3BOxP9n1wMmxBmwuef21gO3i8jbMbHPgWuA0YAN3Xw9YAFgJ5XwD2CQGUjmblbBdFi/y/FC66byIiEjvoIBNutJwYH5s8ssZRWtgcS/w1cRz5wNPufs98fEcEv2yYuAyBngw5VgDCIHS23HdiYRaq1I8DTQD3zGzfmZ2OKs3pZbjTWDLAs9353kREZFeQAGbdKXhtNYaYWaDgC8Sar4AbgD2N7O1zGwsocnvzMT2c2hbk3QQ8Ki7L8s/kLsvBP4fIfh6k9Bk+GQphXT3JuBwwnQafweOBu4oZdsM/wv8IDbN/mfK8912XkREpHewtt12RLqXmf0P8Ja7X17CujOBE919QbF1ezqdFxERSVLAJiIiIlLlSm4SNbM+Zvacmd2d8tyacRLSxWY208yGdmopRURERGpYOX3YTidMzpnmROBdd98KuAy4qKMFExEREZGgpIDNzIYABwBTMlY5BJgW798G7JU3RYKIiIiItFOpNWyXA2cDqzKe3wR4DcDdmwkTe27Q0cKJiIiISJi8syAzO5AwWm2WmY3ryMHMbBIwCWDAgAFjtt12247sTkSk5syaNWu5u29Y6XKISPcqGrABuwIHm9n+QH/gc2b2a3c/NrHO68CmwFIz60tIvv1O/o7c/RrgGoD6+npvbGzsaPlFRGqKmXVZ2jQRqV5Fm0Td/XvuPsTdhwLjgT/kBWsAM4AT4v0j4zqaL0RERESkE5RSw5bKzM4HGt19BnAdcKOZLSbMFD++k8onIiIiUvPKCtjc/VHg0Xj/h4nlnxLS54iIiIhIJ2t3DZuI1LZ//OMfLF26lE8//bTSRemV+vfvz5AhQ+jXr1+liyIiVUABm4i0y9KlSxk4cCBDhw5F0y52LnfnnXfeYenSpWyxxRaVLo6IVIFyMh2IiLT49NNP2WCDDRSsdQEzY4MNNlDtpYi0UMAmIu2mYK3r6NyKSJICNhHpdX74wx/y0EMPddvxxo0bR7F5JS+//HI+/vjjbiqRiPQ2CthEpNc5//zz2XvvvStdjDYUsIlIRyhgE5Ee60c/+hHbbLMNu+22Gw0NDfzkJz8BYMKECdx22238/ve/56ijWmccevTRRznwwAMBeOCBB9hll10YPXo0Rx11FCtWrABg6NChnHvuuYwePZphw4bxwgsvrHbcTz75hPHjx7Pddttx2GGH8cknn7Q8d8opp1BfX88OO+zAueeeC8AVV1zBsmXL2GOPPdhjjz0y1xMRyaKATUR6pGeffZbbb7+duXPnct9996U2Se69997MnDmTjz76CIBbbrmF8ePHs3z5cn784x/z0EMPMXv2bOrr67n00ktbths0aBCzZ8/mlFNOaQkCk37xi1+w9tprs2jRIs477zxmzZrV8twFF1xAY2Mj8+bN47HHHmPevHl85zvfYfDgwTzyyCM88sgjmeuJiGRRwCYi3aZpZRPT5kyjaWVTh/f15JNPcsghh9C/f38GDhzIQQcdtNo6ffv2Zd999+Wuu+6iubmZe+65h0MOOYRnnnmGhQsXsuuuuzJy5EimTZvGX//amqLz8MMPB2DMmDEsWbJktf0+/vjjHHtsyNA3fPhwhg8f3vLcrbfeyujRoxk1ahTPP/88CxcuTC1/qeuJiIDmYRORbjR9/nQm/G4CACeMPKHwyp1k/PjxXHnllXz+85+nvr6egQMH4u7ss88+TJ8+PXWbNddcE4A+ffrQ3Nxc8rH+8pe/8JOf/IRnn32W9ddfnwkTJqROzVHqeiIiOaphE5Fu0zCsgamHTKVhWEOH97Xrrrty11138emnn7JixQruvvvu1PW++tWvMnv2bK699lrGjw9pjnfeeWeefPJJFi9eDMBHH33Eiy++WPKxd999d/7v//4PgAULFrQ0Z37wwQcMGDCAddddlzfffJP77ruvZZuBAwfy4YcfFl1PRCSNathEpNvU9anrtJq1nXbaiYMPPpjhw4ez0UYbMWzYMNZdd93V1uvTpw8HHnggU6dOZdq0aQBsuOGGTJ06lYaGBj777DMAfvzjH7P11luXdOxTTjmFiRMnst1227HddtsxZswYAEaMGMGoUaPYdttt2XTTTdl1111btpk0aRL77rtvS1+2rPVERNKYu1fkwPX19V5s3iIRqV6LFi1iu+22q2gZVqxYwTrrrMPHH3/M7rvvzjXXXMPo0aMrWqbOlHaOzWyWu9dXqEgiUiGqYRORHmvSpEksXLiQTz/9lBNOOKFXBWsiIkkK2ESkx8r1IxMR6e006EBERESkyilgExEREalyCthEREREqpwCNhEREZEqVzMBW9PKJq6bfR2/bPwlv2z8JdfNvq5T0uOISOVcccUVbLfddqy//vpceOGFANx5551t0jxNnTqVZcuWtTw+6aSTlAZKRHqcmhgl2rSyiZPvPpnr51zfZvkzS5/hqgOuoq5PXYVKJiId8fOf/5yHHnqIIUOGtCy78847OfDAA9l+++2BELDtuOOODB48GIApU6ZUpKwiIh3R62vYmlY2ceo9p7YEa8cPP56rD7iaiSMnMuW5KZx6z6mqaRPpgU4++WReeeUV9ttvPy677DJOO+00nnrqKWbMmMFZZ53FyJEjueiii2hsbOSYY45h5MiRfPLJJ4wbN47cpN3rrLMO3//+9xkxYgQ777wzb775JgAvv/wyO++8M8OGDeMHP/gB66yzTiVfqohI7w7YcsHalOemMHHERKYcNIVrD76Wb9V/i6sPvJqTRp2koE2kh7r66qtb0jytv/76AHz5y1/m4IMP5pJLLmHOnDl897vfpb6+nptuuok5c+aw1lprtdnHRx99xM4778zcuXPZfffdufbaawE4/fTTOf3005k/f36b2jsRkUrp1QHbjXNvDMHayIlcfdDVnDj6xJbmz7o+dVx1wFUK2kS60fLlcMkl4W81qKur48ADDwRgzJgxLFmyBICnn36ao446CoB/+Zd/qVTxRERa1EQftl033TW1n1ouaAOY8twUdttst05LTC0iq7v+ejj77HD/rLMqWxaAfv36YWZASBLf3Nxc4RKJiKTrtQFbrrZsykFTOG7EcZnr5YK2nYfsTPOqZppWNmkQgkgXmTix7d+uMHDgQD788MPMx6XYeeeduf322zn66KO5+eabO7uIIiJl65VNorm+ayfddRJ91+jbEoAtXw6TJ4f/8HO3yZPhg3fr6LtGX0666ySmz59e0bKL9GaDBoWatUGDuu4Y48eP55JLLmHUqFG8/PLLTJgwgZNPPrll0EEpLr/8ci699FKGDx/O4sWLWXfddbuuwCIiJTB3r8iB6+vrPTdSq7NNmzONCb+bwEmjTuKqA67ig3fruPhi+O1vYfHi1dffe28YPqKZlz6Yx55HL+Tb476hWjaRIhYtWsR2221X6WJ0iY8//pi11loLM+Pmm29m+vTp/O53v+v2cqSdYzOb5e713V4YEamook2iZtYfeBxYM65/m7ufm7fOBOAS4PW46Ep3r8hkR00rm2he1cyUg6aw3ybH8T8/quPJJ+Ghh8LzW20Fhx3Wuv5zz4XnHnqoLzCaux5dRt9f3sFpe4yvRPFFpArMmjWL0047DXdnvfXW41e/+lWliyQiNa6UPmyfAXu6+woz6wc8YWb3ufszeevd4u6ndX4Ry3Pj3Bs56a6TuGy3Gznhu3Utgdruu0NdHVx5JWyzTev6y5eHZR9/DLNnr+Thhw/ktxesZPljcNppXdt0IyLV6Stf+Qpz586tdDFERFoUDdg8tJmuiA/7xVtl2lFL9dEG/Gry7sx/Ijzce2+YPj09+Bo0KPRjA1i+vA/HHb+S39/Xhz88DM8+C9OmKWgTERGRyipp0IGZ9TGzOcBbwIPuPjNltSPMbJ6Z3WZmm3ZmIUvVtLKJN5Z8ji/+5iXmP7EZe+8N556bHazlGzQIDjjrN/DVc9n+n1/n3nvhhBOqZ84okWpTqT6wtUDnVkSSSgrY3H2lu48EhgBjzWzHvFXuAoa6+3DgQWBa2n7MbJKZNZpZ49tvv92BYqe75vE7+O9v7cDflqzPttuGQG3y5PJqyCbtfjhTLt2Mb17wCPvut5J77w1zR4lIW/379+edd95RYNEF3J133nmH/v37V7ooIlIlypqHzd3fM7NHgH2BBYnl7yRWmwJcnLH9NcA1EEaJll3aApa92cQ1P/wyLN+MbbZZxZ13rtGupsy6PmGKjzOfOI6fndWXfx47no8+CrVsahoVaTVkyBCWLl1KV/zzJSEgVlosEckpZZTohsA/YrC2FrAPcFHeOhu7+xvx4cHAok4vaREn//cC5j8xmu3GLuXxe4Z0KLhqGNYQ/x7OTxvDfG3qzybSVr9+/dhiiy0qXQwRkZpQSpPoxsAjZjYPeJbQh+1uMzvfzA6O63zHzJ43s7nAd4AJXVPcdMuXw+J5XwBgq5FvdTioqutTR8OwBqbPn84xxzex//6oP5uIiIhUTCmjROcBo1KW/zBx/3vA9zq3aKVZvhyOO34li2YOYdhur3L1+fnd69pn+vzpTPjdBKYeAtOmncAJJ9DSn60aciCKiIhI7ejxuUR/ekUzv7+vL2z5AJPO/zuDN+qcCW9bm0UbqOsTmkOvvBL1ZxMREZFu16NziS5fDnc88CYAo3b6hEm7H95p+67rU8cJI09oSVE1aBAMGADnnaemUREREelePTpgu/JKWDhzEwaPeJ47Lt2r0/N/Nq1sYtqcaTStbAJg4kRa+rNpqg8RERHpLj02YFu+HP74xEoAlq13K4+9dXunHyPXj236/OlAqGWbNi1MxptrGhURERHpaj2yD9vy5aFZ8g8P94Ev3U3Die+29DnrTMl+bDm5ptGzzw5/NQBBRLpK08omps+fHvvSdm4Lgoj0LD2yhu3660Oz5JDRc+HQCey1w4gu+TJLTu+RaxaF0DR68cVw8MFwySWqaRORrpFfyy8itatHBmz7HdDEpmPmsnSXozhpt8M4bsRxXXastC/MQYNCzdqMGaGmTf3ZRKQr7LNxA99471n22bjzWxBEpGfpcU2iy5fDv3zrb7w2awT1O/6Yqw44tEubCtKaRXMmTgx92TTVh4h0hZtuqOPWy+upH6zuFyK1rscFbD+9opn5T2wGWz7AMcc1dXm/jtz0HmnUn01EutLEiW3/ikjt6lFNosuXw+0P/A2AETt9zLfHfaNbjps/vUfSxIkaNSoiXSPX/UK19yLSowK2n17RzKKZQ2DLB5jwzRXdNmqqUMff5IS66ssmIiIiXaFHNYnOfXMeMLpba9egcD82ULOFiIiIdK0eU8O27M0mXn3vVfjq5G6tXYPV01TlGzQoBGvXX69mUREpT6EuFyIiOT0iYGta2cQBp9/L3FsPZeQXR3Zr7VqyDIW+VK+/vv1TfOgLW6R2aa41ESlFjwjYfv7orcx5di0ANl13s4rM+F3sS7Ujk+nm9n3qPacqaBOpMZprTURKUfUBW9PKJqZduw688nUGj1jA1efvWJFyNAxrYOohUzP7sbVnMt1czdoR2x/BSaNOYspzU/RftkiNyc21dtMNSj0lItmqftDBjXNvZM7fXgUOZeLB2zJ4o8oUOZmmqlBev3IGIORq1gCuOuAqdttsN47Y/gimzZmm3IEiNUKDlkSkFFVdw9a0somHF8wF4KBvzuaM71Q2viylr0mpAxCaVjbRvKqZKQdNaQnOThh5ArcvvF39WURqiOZaE5FSVHUN241zb2T6rz4Pj01mxO7NFf9CKza9R05uAAJkZz+YPn86J911ElMPmdqmJq3UY4iIiEjtqNqAraV27bUDAOizRuWLWihNVVIpOUazArNSjyEiIiK1o2qbRG+ceyPTf70mvPJ19t1vJaedVukSBaVMwVFK9oNic7tpqg+R3kOfZxHpqKoN2JpXNcPWv2PHXf/K5Zf1qXhzaE6pcyblpvnI70hc6he35mYS6T30eRaRjqp8O2OGT94fAA9cyoKXNmfGjOy+YN2t1D5muY7Ey5eHedkmTgzLkiNDCzV9qi+bSO8R5lrbgX02Hl7poohID1WVAVvTyib+cMv28NJo9tprJRMn9ql0kVqU28csfwBCqYGY+rKJ9B65udbqB1fPP58i0rNUZcB249wbuevFV4HR7LZb9TSH5jStbCo6H1tO/hxLCsREao/mWhORjqraPmzsOJ1hu71KQxW2CJbTHyU5L9uyN8vreKyOyiK9g+ZaE5GOqroatqaVTXz47poMm/sQ85/YrKr6r+WU278s1yzauGwet643ASjcfy2n1P5uIiIi0rsVDdjMrD/wOLBmXP82dz83b501gRuAMcA7wNHuvqQ9Bbpx7o2c+aPX4Ilj2Xvv6mxCKLdZM/cajjl+OPu/kZ2PNJ8GHoj0LOV0lxARKUcpTaKfAXu6+whgJLCvme2ct86JwLvuvhVwGXBRewv04btrwmu7ALDrrtXdhFBqk2WuOaSuTx1vPXgCH7xb2hd5sbnaRKS6aPoOEekqRWvY3N2BFfFhv3jzvNUOASbH+7cBV5qZxW3LsuJPR8Era/L1fZs57bSqa7Fto9Qmy9x/3cvuP4b/Oie8pmpr5hWRjtP0HSLSVUqKiMysDzAL2Aq4yt1n5q2yCfAagLs3m9n7wAZAgfTnGQXa7h74Uh3jvtXEoEGHl7t5tyq1yTIX2P1s9zW5+OLxVdnMKyIdp+k7RKSrlBSwuftKYKSZrQf81sx2dPcF5R7MzCYBkwA222yz1Z5vWtnEU/cOhZdG89HsZji03CN0r7o+dTQMayjaZ6U1sDucuj26s4Qi0p00fYeIdJWypvVw9/eAR4B98556HdgUwMz6AusSBh/kb3+Nu9e7e/2GG2642v6vefwO7nr4baA6kr2XoliflfxOyLnMB8vLrnsUkWqn6TtEpKsUDdjMbMNYs4aZrQXsA7yQt9oMINeJ60jgD+3pv/beM4fBK1+P/dfK3boyGoY1MOWgKTSvak4dfJAf0OWm+MhKCp9G87GJiIjUtlKqsTYGpsV+bGsAt7r73WZ2PtDo7jOA64AbzWwx8HdgfLsK04P6r+XU9amj7xp9mfC7CfRdo2/L4INczdoR2x8BtDaLtqfJRPOxiVQXTd8hIt2tlFGi84BRKct/mLj/KXBURwvz/qwD4KU1eX/WZ1Xffy2pYVgDzauaW2rZ6vrUZQZZWUnhi+0/+VdEKkv/RIlId6uajmLLl8OMB5cDm7Do7UWEKd96hmQtW07zqmamHDQlM8jKTwpfbP/6URCpHpq+Q0S6W9UEbD+9opmFMzdhu7FL+fl521e6OGXLBWbNq5o56a6TAJh6yNTM5hKNJhPpuTR9h4h0t6oJ2Oa/tQAYydajljN4oyGVLk7ZcrVgyYEBhZowc02jItLz6B8uEelu1o7BnJ2ivr7eGxsbWx7PXrCCQye8zJ1T/4nRO65TkTJ1t+XLQ9NoKf3YREQAzGyWu9dXuhwi0r3KmoetqyxfDhNPeZfXZo3gouvyZwzpvdozxYeIiIjUnqoI2K68EuY9sSmDRzzPRWdtW+nidJuJE+Hii0tvVtF8bCLdR583EakmVRGwffxx+LtswD089tbtlS1MN0r2Yysl+0GxrAoi0nmuefwOJpy9gGsev6PSRRERqfygg+XL4bnnwv1Dhn2NhmE9b4RoR5U6xYfmYxPpPh/+6Uh4cDwf7tUMygEsIhVW8YDt+uvhoYdg+G6v8fPztq/JWcNLHXGm+dhEus83T+xL3zVg4sSKf02KiFS+SXTiRPjGGY3M22UUD75Rm019ShgtUn30uRSRalLxgG3QILjuom05abfDWvJu1qJcqqpi/dhERESk9lQ8YAO4feHtTHluCrcvrJ0BB/k0xYeIiIhkqYrOGepMr5nTRSpl2ZtNnHnhPC47ZziDN6q9PrQi0jNURcAmSlUlUilnXjiPWy+vBxq55TIlEBCR6lQVTaKaX6xVsb5smsxTpHNdds5wvnFGI5edM7zSRRERyVQVNWxqEm1VbE62XHALaIoPkU4weKM61ayJSNWreMDWtLKJ6fOn0zCsoSbnYMtXrC+bglsREZHaU/GATTVGbRXry6bJc0VERGpPxfuwNQxrYOohU1VjlEfzsol0rmVvNnH0mY0se1P9P0Wk56l4wJarMVJzaFual02kc+VGg5554bxKF0VEpGwVbxKVdJqXTaRzhVGgGg0qIj2TuXtFDlxfX++NjY0VObaISE9lZrPcXcNaRWpMxZtEJZv6sYmIiAgoYKtq6scm0j76Z0dEehsFbFVs4kS4+OL0fmzKeCCS7drrmjn77PBXRKQ30KCDKlZoTjbNXyeSbeDY22CfWQwcOwYYX+niiIh0mAK2HmD58tAsOnFiCOJAGQ9ECpm0++EMXP8zGoYdXumiiIh0iqJNoma2qZk9YmYLzex5Mzs9ZZ1xZva+mc2Jtx92TXFrU1pfNs1fJ5JNnw8R6W1KqWFrBv7D3Web2UBglpk96O4L89b7o7sf2PlFFM3JJiIiUtuK1rC5+xvuPjve/xBYBGzS1QWTVrm+bLnmUBEJNBpURGpFWaNEzWwoMAqYmfL0LmY218zuM7MdOqNw0pZ+nETa0mhQEakVJQdsZrYOcDtwhrt/kPf0bGBzdx8B/Ay4M2Mfk8ys0cwa33777XYWuXZpXjaRtsJo0LPCXxGRXqyk1FRm1g+4G7jf3S8tYf0lQL27Z9YFKTVV+dJGi4rUsqaVTUyfP52GYQ01M8BAqalEalPRgM3MDJgG/N3dz8hY54vAm+7uZjYWuI1Q45a5cwVsIiLlU8AmUptKaRLdFTgO2DMxbcf+ZnaymZ0c1zkSWGBmc4ErgPGFgjVpv/x+bMp4ILVA/TdFpNYVndbD3Z8ArMg6VwJXdlahJFuuHxuEkaPKeCC14Nrrmvmvc/rSvKqZ731X832LSO3RN18Pkz8nmzIeSC1QqikRqXUlDTroCurDJiKlqsXBBVnUh02kNpU1D5tUD/XpkVqiVFMiUusUsPVQmpNNeiv9MyIisjr1YeuhlF9Ueqv8gTUiIqKArcfK5RcV6W2OOb6JxmXzOOb44YCaQEVEQE2iPZ6aj6S3efCN6dy63k48+Mb0ShdFRKRqqIath8s1HzWvambw12/SKDrpMbJSrWmqGhGR1Slg6+FyfdjWqr9NE+hKj5LVVy03IlRERFopYOvhcn3ZmlYezsD1p6pWQnoMDZwRESmd+rD1EpqnSnqaz63fxBf2mcbn1lceXBGRYhSw9RIafCA9TS4P7vT5GlwgIlKMmkR7Cc1dJdUsbYCBBheIiJROAVsvof5AUs3S/qHQ4AIRkdIpYOslNJGuVDP9QyEi0jHqw9bLqC+bVCMNMBAR6RgFbL3Mtdc1c/bZ4a9IJaT906ABBiIiHaMm0V5m4NjbYJ9ZDBw7Bhhf6eJIDUrrr6YBBiIiHaOArZeZtPvhDFz/MxqGHV7pokiNSuuvpgEGIiIdoybRXib3w/jBu3XqyyYVof5qIiKdTwFbL5Vrlrr++kqXRHoz9VcTEekeahLtpTSNgnQH9VcTEeke5u4VOXB9fb03NjZW5Ngi0jnSMhhI1zKzWe5eX+lyiEj3Ug2biLSbJmwWEeke6sPWy2kiXelKTSubmDZnGk0rNcBARKQrKWDrpXI/pLmJdDX4QDpKAwxERCpHTaK9VO6H9Ge7r8nFF4/X4APpMA0wEBGpnKIBm5ltCtwAbAQ4cI27/zRvHQN+CuwPfAxMcPfZnV9cKVXrD+nh1O1R4cJIr6AJcUVEKqeUJtFm4D/cfXtgZ+BUM9s+b539gC/F2yTgF51aSilb7oe0rk8doL5s0nGaEFdEpHKKBmzu/kautszdPwQWAZvkrXYIcIMHzwDrmdnGnV5aaTdNpCvlUH81EZHqUlYfNjMbCowCZuY9tQnwWuLx0rjsjY4UTjqPJtKVcqi/mohIdSk5YDOzdYDbgTPc/YP2HMzMJhGaTNlss83aswtpJ82XJeVQfzURkepS0rQeZtaPEKzd5O53pKzyOrBp4vGQuKwNd7/G3evdvX7DDTdsT3mlTPnzZKkvm5RC/dVERKpL0YAtjgC9Dljk7pdmrDYDON6CnYH33V3NoVUgv9+R+rJJPvVXExGpfqU0ie4KHAfMN7M5cdl/AZsBuPvVwL2EKT0WE6b1UE+pKpHf70h92SSf+quJiFQ/JX8XqXFK4N6zKPm7SG1Saqoapb5skqP+aiIi1U8BW41SX7balR+sq7+aiEj1Uy7RGqW+bLUrv8+a+quJiFQ/9WGrEU0rm5g+fzoNwxpa0lVJbVKftZ5NfdhEapOaRGuEmr0kR33WRER6HgVsNaJhWANTD5m6WrOXBh/0bppjTUSkd1AfthqRlVYobQ4u6T00x5qISO+ggK3GafBB73bM8U00LpvHMccPB0LfReUEFRHpedQkWuNySeHV+bx3evCN6dy63k48+IaaP0VEejIFbDUmPxl8kvqz9Xz51zCr76KIiPQsCthqTKEO55pMt+fLv4a55k9N5SIi0rOpD1uNKdThXP3Zer60PmsiItLzqYatxhSqccn1ZwM1jfZU6rMmItI7qYZNVqOpPnqGtIwFmrJDRKR3Ug1bjSo0+GDiRLj4YjWNVrtrr2vm7LPD3xz1WRMR6Z1Uw1ajcoMPgNXm5Eo2jUr1Gjj2NthnFgPHjgHGV7o4IiLShRSw1ahSms6UJLy65F+PSbsfzsD1P6Nh2OGVLpqIiHQxNYnWqFKazjTNR3XJbwJV86eISO1QDZtk0jQf1UVNoCIitUsBW41rWtnE9PnTaRjWsFpNjfqyVRc1gYqI1C41ida4QpkPkpS2qvste7OJo89sZNmbYSSvmkBFRGqXathqXKnzdmlutu535oXzuPXyeqCRWy6rr3RxRESkghSw1bhcrU0x6s/WtdKapi87ZzjQGP+KiEgtU5OotCg0ma7SVnWtax6/gwlnL+Cax+9oWTZ4ozpuuayewRupCVREpNYpYJMWpfRn01QfXePDPx0JD14S/oqIiORRk6i0KKU/m5pGO0d+E+g3T+xL3zVg4kR9JEVEZHXm7hU5cH19vTc2Nlbk2CKVduUjN/Nv/zuLn31vDKftoTnVpHRmNsvdNQpFpMaoSVRWU6gvW46m+ShP/jlVE6iIiJSjaPuLmf0KOBB4y913THl+HPA74C9x0R3ufn4nllG6WaHE8Dma5qM8+edUTaAiIlKOUn4tpgJXAjcUWOeP7n5gp5RIKk592Touv49a/jlVFgkRESlH0SZRd38c+Hs3lEWqRHJutmLTfAwapObRNPkjbj94t463HjyBD97VFB0iIlK+zurDtouZzTWz+8xsh07ap1RYqWmrNNVHkOyn1jCsgamHTG2pUdM5EhGRjuiMDjSzgc3dfYWZ7Q/cCXwpbUUzmwRMAthss8064dDSlUpNW6Xm0SDZT+2AISeEGrUhoRZS50hERDqiwzVs7v6Bu6+I9+8F+pnZoIx1r3H3enev33DDDTt6aOliyWTjyoKwuvxzkqxVy69RSzYhi4iIlKvDNWxm9kXgTXd3MxtLCALf6XDJpKpo5Ojq8s9Jsu+fatRERKQzlTKtx3RgHDDIzJYC5wL9ANz9auBI4BQzawY+AcZ7pWbjlS6TaxY9YvsjmDZnWpsk5Tm9PUgpNvJz+fIQtE6cqFGgIiLSuYoGbO5esAOTu19JmPZDerFc7dG0OdMya9qSQUp+8NIbFKpRg9qrYRQRke6jWTulLKXUtEHvCF6K1ajl6+01jCIiUjlKTSVlydUq3b7w9oJTfkycCBdfHP72pHnakgMJ8qc1SQ7CgNVflwYWiIhIV1ENm7RLsrYpvyYKVh852lNq25LNnsVq1HpDLaKIiPQMCtikXfKzIRQaQZprIjz44BC8VVO/tkLNnvl91PL75akJVEREuouaRKXDcvOP5fq15c/VlqttmzGjOmb7L6fZM0lzq4mISKWohk06LG0EacOwhtWaSZM1Ut09ijRZk1Zqs6dq1EREpFooYJNOkwx+8oOiXLB01lkheOuKfm3JoAxoEzBmBWn5zZ5J+X3UNLeaiIhUigI26TTJ4CcteGte1UzfNfrSMKyBY46HxmXzOOb44SxfXtfu2rasmjOgTb+6UoO0ZK2aatRERKRaKGCTLpEWvDWvam4TUN263gT2f2Mqy+4/hv86py/Nq5o5YcIqzrxwHpedM5xBg0itMUveL9a8mbufFaTlN3vm16qpRk1ERKqBAjbpcrlgqWllU0sNW07DsAauefc22GcWA8eO4cwLt+LWy+uBRvY/4fnUGrPk/UI1Z6XUouUHaKpVExGRaqSATbpNVkA1affDGbj+ZzQMO5zDtwdo5LJzhgPD+cZ7O7DPxsMBWu7nmk2LNW8mZQVp+QGa+qmJiEg1UsAmFZcMugZvBLdcVg+EgQm3Xl5P/eCwXu7+xInw1oMn8MGQsDwXiGXdz2/qTAZpCtBERKQnUMAmVSuteTK/hgyK389v6lSQJiIiPY0CNqla+YFV7n5WIFfovoI0ERHpyczdK3Lg+vp6b2xsrMixRUR6KjOb5e71lS6HiHQvpaYSERERqXIK2ERERESqnAI2ERERkSqngE1ERESkyilgExEREalyCthEREREqpwCNhEREZEqp4BNREREpMopYBMRERGpcgrYRERERKqcAjYRERGRKqeATURERKTKFQ3YzOxXZvaWmS3IeN7M7AozW2xm88xsdOcXU0RERKR2lVLDNhXYt8Dz+wFfirdJwC86XiwRERERySkasLn748DfC6xyCHCDB88A65nZxp1VQBEREZFa1xl92DYBXks8XhqXiYiIiEgn6NudBzOzSYRmU4AVZvbn7jx+FxoELK90ISpEr7026bVXzuYVPLaIVEhnBGyvA5smHg+Jy1bj7tcA13TCMauKmTW6e32ly1EJeu167bWmll+7iFROZzSJzgCOj6NFdwbed/c3OmG/IiIiIkIJNWxmNh0YBwwys6XAuUA/AHe/GrgX2B9YDHwMTOyqwoqIiIjUoqIBm7s3FHnegVM7rUQ9U69r5i2DXntt0msXEelGFuItEREREalWSk0lIiIiUuUUsHVAsbRdvZWZbWpmj5jZQjN73sxOr3SZupOZ9TezP5nZ3Pj6z6t0mbqbmfUxs+fM7O5Kl6U7mdkSM5tvZnPMrLHS5RGR2qEm0Q4ws92BFYRMDztWujzdJWay2NjdZ5vZQGAWcKi7L6xw0bqFmRkwwN1XmFk/4Ang9JjpoyaY2b8D9cDn3P3ASpenu5jZEqDe3Wt1DjoRqRDVsHVACWm7eiV3f8PdZ8f7HwKLqKHsFjEN24r4sF+81cx/PmY2BDgAmFLpsoiI1AoFbNIhZjYUGAXMrHBRulVsEpwDvAU86O619PovB84GVlW4HJXgwANmNitmbhER6RYK2KTdzGwd4HbgDHf/oNLl6U7uvtLdRxIye4w1s5poEjezA4G33H1WpctSIbu5+2hgP+DU2C1CRKTLKWCTdol9t24HbnL3Oypdnkpx9/eAR4B9K1yU7rIrcHDsy3UzsKeZ/bqyReo+7v56/PsW8FtgbGVLJCK1QgGblC12ur8OWOTul1a6PN3NzDY0s/Xi/bWAfYAXKlqobuLu33P3Ie4+FBgP/MHdj61wsbqFmQ2Ig2wwswHA14CaGiEuIpWjgK0DYtqup4FtzGypmZ1Y6TJ1k12B4wi1K3Pibf9KF6obbQw8YmbzgGcJfdhqanqLGrUR8ISZzQX+BNzj7r+vcJlEpEZoWg8RERGRKqcaNhEREZEqp4BNREREpMopYBMRERGpcgrYRERERKqcAjYRERGRKqeATURERKTKKWATERERqXIK2KRXMbNhZvZXMzuljG3WMrPHzKxPJxy/zsweN7O+Hd2XiIhIjgI26VXcfT4hZdLxZWz2r8Ad7r6yE47fBDwMHN3RfYmIiOQoYJPe6C1ghzLWPwb4Xe6BmT1qZtvG+xuY2YJ4/zdmdqWZPRFr8XYzsxvN7EUzuy6xvzvjPkVERDqFAjbpjS4E1jSzzfOfMLN7zWxw4nEdsKW7L0msthXwYrw/HJgf7w8DXnH33YBfAtcBZwPbAweY2ZpxvQXATp33ckREpNYpYJNexcz2AwYA95BSy+bu+7v7ssSiQcB7ie03B15391Vx0XBgnpn1B9YDLs/tCrjO3d9w92ZgJdAUj7ESaDKzgZ33ykREpJYpYJNeIwZVFwHfJtSK7VjCZp8A/ROPRwDzEo/HxMc7ALMTgdwIYGY87hBgmbt7Yrs1gU/b8TJERERWo4BNepMfADfE5s2SAjZ3fxfoE4M9gJHEAM7MvgQcEvc1DJib2HQ4rYFdmyDPzDYAlrv7PzrwWkRERFooYJNewcy2AfahtckyNWDL78MWPQDsFu+PANYws7nAD4GFwAmEgG1O3Ed/YK0Y7EHb4A1gD0KTrIiISKewtq04IrXHzEYDZ7r7cWb2EjDa3T/swP7uAM5x9xeLriwiIlIC1bBJzXP32cAjZrZueNihYK0OuFPBmoiIdCbVsImIiIhUOdWwiYiIiFQ5BWwiIiIiVU4Bm4iIiEiVU8AmIiIiUuUUsImIiIhUOQVsIiIiIlVOAZuIiIhIlVPAJiIiIlLl/j8T7rXDJ7X5QAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 93600x93600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wavelengths = df.iloc[:, 0]\n",
    "refractive = df.iloc[:, 1]\n",
    "coeff = np.polyfit(wavelengths, refractive, 13)\n",
    "fitted = np.poly1d(coeff)\n",
    "print(coeff)\n",
    "plt.plot(wavelengths, refractive, 'gx', label=\"given data\", markersize=1.2)\n",
    "plt.plot(wavelengths, fitted(wavelengths), 'bo', label=\"fitting\", markersize=1)\n",
    "errors = fitted(wavelengths) - refractive\n",
    "#plt.errorbar(wavelengths, refractive, yerr=errors)\n",
    "plt.xlabel(r\"$\\lambda :(\\mu m )$\")\n",
    "plt.xlim(0.2, 5.5)\n",
    "plt.ylim(1, 4)\n",
    "plt.title(r\"$n(\\lambda)$ and fit $n(\\lambda)$\")\n",
    "plt.annotate(repr(fitted), xy=(3, 3), xytext=(3, 5))\n",
    "plt.legend()\n",
    "plt.figure(figsize=(1300, 1300))\n",
    "print(fitted)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-87eb775ee198>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mwave\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwave\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "wave = df.iloc[:, 0]\n",
    "n = df.iloc[:, 1]\n",
    "k = df.iloc[:, 2]\n",
    "plt.plot(n, k, wave)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " [2.3]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.constants as sc\n",
    "nx=950\n",
    "nt=1000\n",
    "delta_x=1e-9\n",
    "x=np.linspace(0, nx*delta_x,nx)\n",
    "Sc = 1\n",
    "c = sc.c\n",
    "delta_t=delta_x * Sc/c\n",
    "epsilon0 = sc.epsilon_0\n",
    "epsilon_r = 2.3\n",
    "#epsilon = np.random.rand(nx).reshape(nx, 1) + 1\n",
    "epsilon = np.ones((nx, 1)) * epsilon_r\n",
    "#epsilon[int(nx/2-5):int(nx/2+5)] \n",
    "mu0 = sc.mu_0\n",
    "e = np.zeros((nx, 1))\n",
    "h = np.zeros((nx, 1))\n",
    "t0 = 10\n",
    "a = 5\n",
    "plt.figure()\n",
    "plt.ion()\n",
    "print(epsilon)\n",
    "fig, ax = plt.subplots(2, 1)\n",
    "ax[0].set_title('E and H alone X')\n",
    "ax[0].set_ylim([0, 1.4])\n",
    "ax[0].set_ylabel(r\"E\")\n",
    "\n",
    "ax[0].set_xlabel(r\"length alone x:(m)\")\n",
    "\n",
    "for i in np.arange(0, nt):\n",
    "    e[0]=1\n",
    "    h[0:nx-1]=h[0:nx-1] + (1 / Sc) * (e[1:nx] - e[0:nx-1])\n",
    "    #print(h.shape, e.shape, epsilon.shape)\n",
    "    e[1:nx] = e[1:nx] +  (Sc / epsilon[1: nx]) * (h[1:nx] - h[0:nx - 1])\n",
    "    h[nx - 1] = h[nx - 2]\n",
    "\n",
    "#plt.clf()\n",
    "\n",
    "    #ax[0].plot(x,e)\n",
    "    \n",
    "    #plt.ylim([-2, 2])\n",
    "    #ax[1].plot(x, h, 'g-')\n",
    "# plt.savefig(fname=\"scatter\"+str(i)+\".png\")\n",
    "ax[0].plot(x, e)\n",
    "ax[1].plot(x, h)\n",
    "plt.pause(0.1)\n",
    "\n",
    "plt.ioff()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#FDTD using python-fdtd\n",
    "import os\n",
    "import fdtd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "invalid grid shape (3e-09, 1.5e-09)\ngrid shape should be a 3D tuple containing floats or ints",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-1e8ca4a2a03c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mgrid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfdtd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3e-9\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1.5e-9\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Python38\\lib\\site-packages\\fdtd\\grid.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, shape, grid_spacing, permittivity, permeability, courant_number)\u001b[0m\n\u001b[0;32m    127\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    128\u001b[0m         \u001b[1;31m# save grid shape as integers\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 129\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mNx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mNy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mNz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    130\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    131\u001b[0m         \u001b[1;31m# dimension of the simulation:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Python38\\lib\\site-packages\\fdtd\\grid.py\u001b[0m in \u001b[0;36m_handle_tuple\u001b[1;34m(self, shape)\u001b[0m\n\u001b[0;32m    202\u001b[0m         \u001b[1;34m\"\"\" validate the grid shape and transform to a length-3 tuple of ints \"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    203\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 204\u001b[1;33m             raise ValueError(\n\u001b[0m\u001b[0;32m    205\u001b[0m                 \u001b[1;34mf\"invalid grid shape {shape}\\n\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    206\u001b[0m                 \u001b[1;34mf\"grid shape should be a 3D tuple containing floats or ints\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: invalid grid shape (3e-09, 1.5e-09)\ngrid shape should be a 3D tuple containing floats or ints"
     ]
    }
   ],
   "source": [
    "grid = fdtd.Grid(shape=(3e-9, 1.5e-9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "d:\\apmcm\\2021a\\fdtd_output\\fdtd_output_2021-11-28-9-57-1 (GRIN)\n"
     ]
    }
   ],
   "source": [
    "grid = fdtd.Grid(shape=(9.3e-9, 15.5e-9, 1), grid_spacing=77.5e-12)\n",
    "# x boundaries\n",
    "grid[0:10, :, :] = fdtd.PML(name=\"pml_xlow\")\n",
    "grid[-10:, :, :] = fdtd.PML(name=\"pml_xhigh\")\n",
    "# y boundaries\n",
    "grid[:, 0:10, :] = fdtd.PML(name=\"pml_ylow\")\n",
    "grid[:, -10:, :] = fdtd.PML(name=\"pml_yhigh\")\n",
    "simfolder = grid.save_simulation(\"GRIN\")  # initializing environment to save simulation data\n",
    "print(simfolder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "d:\\apmcm\\2021a\\fdtd_output\\fdtd_output_2021-11-28-9-57-3 (GRIN)\\fdtd_output\\fdtd_output_2021-11-28-10-0-55 (GRIN)\n"
     ]
    }
   ],
   "source": [
    "grid = fdtd.Grid(shape=(9.3e-9, 15.5e-9, 1), grid_spacing=77.5e-11)\n",
    "# x boundaries\n",
    "grid[0:10, :, :] = fdtd.PML(name=\"pml_xlow\")\n",
    "grid[-10:, :, :] = fdtd.PML(name=\"pml_xhigh\")\n",
    "# y boundaries\n",
    "grid[:, 0:10, :] = fdtd.PML(name=\"pml_ylow\")\n",
    "grid[:, -10:, :] = fdtd.PML(name=\"pml_yhigh\")\n",
    "simfolder = grid.save_simulation(\"GRIN\")  # initializing environment to save simulation data\n",
    "print(simfolder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid[3.1e-6, 1.5e-6:14e-6, 0] = fdtd.LineSource(period=1000, name=\"source\")#, pulse=True, cycle=3, hanning_dt=4e-15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(-4, 8):\n",
    "    grid[5.8e-9, 84 + 4 * i : 86 + 4 * i, 0] = fdtd.LineDetector(name=\"detector\" + str(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(os.path.join(\"./fdtd_output\", grid.folder, \"grid.txt\"), \"w\") as f:\n",
    "    f.write(str(grid))\n",
    "    wavelength = 3e5/grid.source.frequency\n",
    "    wavelengthUnits = wavelength/grid.grid_spacing\n",
    "    GD = np.array([grid.x, grid.y, grid.z])\n",
    "    gridRange = [np.arange(x/grid.grid_spacing) for x in GD]\n",
    "    objectRange = np.array([[gridRange[0][x.x], gridRange[1][x.y], gridRange[2][x.z]] for x in grid.objects], dtype=object).T\n",
    "    f.write(\"\\n\\nGrid details (in wavelength scale):\")\n",
    "    f.write(\"\\n\\tGrid dimensions: \")\n",
    "    f.write(str(GD/wavelength))\n",
    "    f.write(\"\\n\\tSource dimensions: \")\n",
    "    f.write(str(np.array([grid.source.x[-1] - grid.source.x[0] + 1, grid.source.y[-1] - grid.source.y[0] + 1, grid.source.z[-1] - grid.source.z[0] + 1])/wavelengthUnits))\n",
    "    f.write(\"\\n\\tObject dimensions: \")\n",
    "    f.write(str([(max(map(max, x)) - min(map(min, x)) + 1)/wavelengthUnits for x in objectRange]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 4000 is out of bounds for axis 0 with size 12",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-36-5192f535902c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# running simulation 1 timestep a time and animating\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;36m5\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m         \u001b[1;31m# saving frames during visualization\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Python38\\lib\\site-packages\\fdtd\\grid.py\u001b[0m in \u001b[0;36mstep\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    285\u001b[0m         \u001b[0mupdating\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmagnetic\u001b[0m \u001b[0mfield\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    286\u001b[0m         \"\"\"\n\u001b[1;32m--> 287\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate_E\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    288\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate_H\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    289\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime_steps_passed\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Python38\\lib\\site-packages\\fdtd\\grid.py\u001b[0m in \u001b[0;36mupdate_E\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    309\u001b[0m         \u001b[1;31m# add sources to grid:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    310\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0msrc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msources\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 311\u001b[1;33m             \u001b[0msrc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate_E\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    312\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    313\u001b[0m         \u001b[1;31m# detect electric field\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Python38\\lib\\site-packages\\fdtd\\sources.py\u001b[0m in \u001b[0;36mupdate_E\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    293\u001b[0m         \u001b[1;31m# DISABLED: self.grid.E[self.x, self.y, self.z, 2] = vect\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    294\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mz\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mz\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvect\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 295\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mE\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mz\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    296\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    297\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mupdate_H\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: index 4000 is out of bounds for axis 0 with size 12"
     ]
    }
   ],
   "source": [
    "from IPython.display import clear_output  # only necessary in jupyter notebooks\n",
    "\n",
    "for i in range(100):\n",
    "    grid.step()  # running simulation 1 timestep a time and animating\n",
    "    if i % 5 == 0:\n",
    "        # saving frames during visualization\n",
    "        grid.visualize(z=0, animate=True, index=i, save=True, folder=simfolder)\n",
    "        plt.title(f\"{i:3.0f}\")\n",
    "        clear_output(wait=True)  # only necessary in jupyter notebooks\n",
    "grid.save_data()  # saving detector readings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ffmpeg not installed?\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    video_path = grid.generate_video(delete_frames=False)  # rendering video from saved frames\n",
    "except:\n",
    "    video_path = \"\"\n",
    "    print(\"ffmpeg not installed?\")\n",
    "\n",
    "if video_path:\n",
    "    from IPython.display import Video\n",
    "    display(Video(video_path, embed=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dic = np.load(os.path.join(simfolder, \"detector_readings.npz\"))\n",
    "import warnings; warnings.filterwarnings(\"ignore\") # TODO: fix plot_detection to prevent warnings\n",
    "fdtd.plot_detection(dic)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "2db7dd83ea3461cb8f622e29f5f058e20de98d31c070063ff55f33912d503a58"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "position": {
    "height": "303.4px",
    "left": "736px",
    "right": "20px",
    "top": "21px",
    "width": "350px"
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
